The project (23IND09 MaritimeMET) has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States
About the project
In July 2023, the International Maritime Organization (IMO) revised their 2018 strategy (MEPC 73) target to reduce emissions further through a commitment to ensure the uptake of alternative zero and near-zero greenhouse gas (GHG) fuels by 2030. To achieve this, metrological support is needed to accelerate the deployment of sustainable fuels like methanol, dimethylether, and ammonia in this sector.
Currently, the sector requires traceable measurements of emission components according to the International Convention for the Prevention of Pollution from Ships (IMO MARPOL), Regulations 13 and 14. There is an inherent need to ease the availability of traceable standards, check the compatibility and ability of existing and new emission monitoring techniques to accurately measure pollutants from future fuels, along with a better understanding of instrument characterisations and their uncertainties (i.e., span drift, instrument linearity, and cross interferences). Calibration of measurement systems using gas standards (required below 2 % uncertainty) can be substituted by spectroscopy-based techniques. However, these are only established for specific species (e.g. NO2) at limited pressure, temperature, and gas matrix operating conditions and must be explored for existing and new species relevant to future fuels.
The transition to renewable energy carriers necessitates further engine system development and retrofitting of existing power units and after-treatment systems. These activities require traceable measurements of dynamic pressure and temperature at engine operating ranges to monitor and optimise the in-cylinder processes for efficient operation. The dynamic sensors used for this purpose are quasi-statically calibrated, which can lead to significant uncertainties and require traceable measurement methods.
Engine manufacturers require accurate sensors with traceable measurement methods to reduce uncertainties during the R&D phase. There is a need to increase the reliability of low-cost sensor data deployed in different engine systems through machine learning approaches. However, machine learning models require large amounts of accurate and traceable data. The combined needs must be addressed through testing low-cost and high-quality sensor techniques in application test benches, and in the process, this can lead to exploring cost-effective virtual sensor concepts.
THE OBJECTIVES
The MaritimeMET project has 4 specific objectives:
To develop new and improve existing traceable emission measurement methods for online and in-situ measurements of typical gaseous (e.g., NO, NO2, N2O, NH3, CH3OH, CO, CH2O) and PM, black carbon (BC) emissions generated with the use of Power-to-X (PtX) fuels (e.g. methanol, dimethylether, ammonia).
The methods and selected commercial low-cost sensors will be validated and applied for dynamic measurements in industrial environments such as test engines running on selected fuels. Sources of measurement uncertainties will be identified and quantified. Measured quantities will be used in predictive model developments using machine learning tools.
To establish quality-assured dynamic measurements of the in-cylinder dynamic pressure and temperature necessary for assessing and optimising the quality and efficiency of the energy conversion processes using renewable fuels.
This will be achieved by developing primary standards and robust measurement methods covering the 0.1 MPa – 30 MPa pressure range and up to 2500 °C range in temperature. The frequency ranges of 0.5 kHz – 100 kHz and up to 1 kHz in pressure and temperature, respectively, with an uncertainty of 1 % will be targeted.
An inter-laboratory comparison and engine tests will validate the developed standards and methods. The results will be used in the predictive model development.
To create predictive models for engine emissions and performance using chemical kinetics and machine learning. Furthermore, virtual sensor concepts will be developed based on data-driven and physics-based models to estimate hard-to-measure quantities or substitute costly sensors.
Emission measurements and in-cylinder pressure and gas temperature will deliver the required database for model development, a mandatory input to complete these objectives.
All models will be validated, and uncertainty estimations will be done.
To facilitate the take up of the technology and measurement infrastructure developed in the project by the measurement supply chain (e.g. accredited laboratories, instrument manufacturers), standards developing organisations (e.g. ISO, CEN/CENELEC), end users (e.g. marine, power and aviation industries), and via the European Metrology Networks (such as Energy Gases, Pollution Monitoring, Climate and Ocean Observation, and Mathematics and Statistics).
